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dc.contributor.authorWei, X-
dc.contributor.authorYang, S-
dc.contributor.authorYu, K-
dc.contributor.authorYang, X-
dc.identifier.citationJournal of Statistical Planning and Inference, 140 pp. 1621 - 1634, 2010en_US
dc.description.abstractBahadur representation and its applications have attracted a large number of publications and presentations on a wide variety of problems. Mixing dependency is weak enough to describe the dependent structure of random variables, including observations in time series and longitudinal studies. This note proves the Bahadur representation of sample quantiles for strongly mixing random variables (including ½-mixing and Á-mixing) under very weak mixing coe±cients. As application, the asymptotic normality is derived. These results greatly improves those recently reported in literature.en_US
dc.format.extent1621 - 1634-
dc.titleBahadur representation of linear kernel quantile estimator of VaR under mixing assumptionsen_US
dc.relation.isPartOfJournal of Statistical Planning and Inference-
Appears in Collections:Dept of Mathematics Research Papers

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